Fusion of Partition Local Binary Patterns and Convolutional Neural Networks for Dorsal Hand Vein Recognition

Although deep learning algrithms have outstanding performance in biometrics and been paid more and more attention, triditional features for should not be ignored. In this paper, fusion of partition local binary patterns (PLBP) and convolutional neural networks (CNNs) is investigated in three schemes. In serial fusion (SF) method, PLBP feature is extracted and reshaped as the input of CNNS. Decision fusion (DF) carries out the PLBP with nearest neighour classifer and CNNs seperatelly and weighted fuses the results. For feature fusion (FF), PLBP feature is reshaped and weighted fused with the feature map of CNNs. To examine the proposed methods, NCUT data set with 2040 images from 204 hands is augmented using PCA. The results indicate that when the PLBP and CNNs are merged in FF scheme with weights of 0.2 and 0.8, our fusion method reaches a state-of-the-art recognition rate of 99.95%.